Master Thesis Robust Identification of Compositional Electrical Drive Models - Identifiability, Sensitivity & Excitation Analysis

• During your thesis, you delve into physics-based models of electric drives and our Python component library while simultaneously reviewing current literature on robust system identification and the design of experiments.
• Furthermore, you develop innovative robustness diagnostics for our existing identification pipeline - considering parameter identifiability, sensitivity, and convergence behavior - and implement as well as quantify appropriate countermeasures.
• In Addition, you analyze the excitation content of given datasets, evaluate relevant criteria, and link these insights to the achievable identification quality.
• You comprehensively evaluate the approach you have developed using a practical benchmark use case.
• Moreover, you document as well as present your research findings clearly and understandably.
• Lastly, you extend the approach to address partially observable effects resulting from states that cannot be measured directly, such as temperatures.

Function: Research
Experience Level: Not Applicable